Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography

摘要: A series of papers has developed a statistical mechanics neocortical interactions (SMNI), deriving aggregate behavior experimentally observed columns neurons from electrical-chemical properties synaptic interactions. While not useful to yield insights at the single neuron level, SMNI demonstrated its capability in describing large-scale short-term memory and electroencephalographic (EEG) systematics. The necessity including nonlinear stochastic structures this development been stressed. Sets EEG evoked potential data were fit, collected investigate genetic predispositions alcoholism extract brain ``signatures'' memory. Adaptive simulated annealing (ASA), global optimization algorithm, was used perform maximum likelihood fits Lagrangians defined by path integrals multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for an individual's data. CMI give better signal recognition than raw data, can be advantage as correlates behavioral states. These results strong quantitative support accurate intuitive picture, portraying having common algebraic or physics mechanisms that scale across quite disparate spatial scales functional phenomena, i.e., among neurons, regional masses neurons.